Setting and Revising Antibacterial Susceptibility Breakpoints

Transcription

1 CLINICAL MICROBIOLOGY REVIEWS, July 2007, p Vol. 20, No /07/$ doi: /cmr Copyright 2007, American Society for Microbiology. All Rights Reserved. Setting and Revising Antibacterial Susceptibility Breakpoints John Turnidge 1 * and David L. Paterson 2,3,4 Division of Laboratory Medicine, Women s and Children s Hospital, North Adelaide, Australia 1 ; Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 2 ; University of Queensland, Brisbane, Australia 3 ; and Queensland Health Pathology Services, Royal Brisbane and Women s Hospital, Brisbane, Australia 4 INTRODUCTION DEFINITIONS OF SUSCEPTIBILITY CATEGORIES ORGANIZATIONS THAT SET BREAKPOINTS THE NATURE OF MICs DATA NEEDS FOR SETTING BREAKPOINTS MIC Distributions and Wild-Type Cutoff Values Phenotypic and Genotypic Resistance Markers PK/PD Considerations In vitro studies Animal model studies Clinical studies of PD Estimation of target attainment Outcome Data from Clinical Studies COMBINING CUTOFFS TO SET BREAKPOINTS General Principles Breakpoints by Infection Site Urinary tract Cerebrospinal fluid Breakpoint Setting before Resistance Has Emerged Reevaluation of Breakpoints Years after the Commercial Release of an Antibacterial Agent SETTING ZONE DIAMETER BREAKPOINTS FOR DISK DIFFUSION TESTING UNANSWERED QUESTIONS AND FUTURE NEEDS CONCLUSIONS REFERENCES Downloaded from INTRODUCTION Breakpoints are an integral part of modern microbiology laboratory practice and are used to define susceptibility and resistance to antibacterials. Depending on the testing method, they are expressed as either a concentration (in mg/liter or g/ml) or a zone diameter (in mm). In general, all susceptibility testing methods require breakpoints, also known as interpretive criteria, so that the results of the tests can be interpreted as susceptible, intermediate, or resistant and reported as such to a broad range of clinicians. It is acknowledged that sophisticated prescribers may not require (or desire) breakpoints but rather utilize the MIC and knowledge of the pharmacodynamics (PD) of the antibacterial in question to optimize antibacterial selection and dosing. However, given the volume of specimens that a typical clinical microbiology laboratory receives and the diversity of clinicians that a laboratory serves, categorical interpretation of antibacterial susceptibility testing results is a practical necessity and is preferred by most clinicians. The term breakpoint has been used in a variety of ways in the literature (141). The first and most obvious one refers to * Corresponding author. Mailing address: Division of Laboratory Medicine, Women s and Children s Hospital, 72 King William Rd., North Adelaide, South Australia, Australia. Phone: Fax: john.turnidge@cywhs.sa.gov.au. the MIC for any given antibacterial that distinguishes wild-type populations of bacteria from those with acquired or selected resistance mechanisms ( wild-type breakpoints; sometimes called microbiological breakpoints). Data for deriving this type of breakpoint are generated from moderate to large numbers of in vitro MIC tests, sufficient to describe the wild-type population. In this context, the wild-type strain is defined as a strain of a bacterium which does not harbor any acquired or selected resistance to the particular antibacterial being examined or to antibacterials with the same mechanism/site of action. The second are so-called clinical breakpoints, which refer to those concentrations (MICs) that separate strains where there is a high likelihood of treatment success from those bacteria where treatment is more likely to fail. In their simplest form, these breakpoints are derived from prospective human clinical studies comparing outcomes with the MICs of the infecting pathogen. The third use of the term breakpoint refers to antibacterial concentrations calculated from knowledge of a PD parameter and the dimension of that parameter that predicts efficacy in vivo. These are the pharmacokinetic/pd (PK/PD) breakpoints, where data that have been generated in an animal model are extrapolated to humans by using mathematical or statistical techniques. Recently, in an attempt to reduce confusion about the meaning of the term breakpoint, the European Committee on Antimicrobial Susceptibility Testing proposed the use of the term epidemiolog- on October 5, 2018 by guest 391

2 392 TURNIDGE AND PATERSON CLIN. MICROBIOL. REV. ical (or wild-type) cutoff value to replace the term microbiological breakpoint (82). We propose that the term cutoff be used more widely to describe the three types of breakpoints and that the term breakpoint be reserved for the final selected value to be applied in the clinical laboratory. Hence, we prefer the terms wild-type cutoff, PK/PD cutoff, and clinical cutoff to describe these entities. DEFINITIONS OF SUSCEPTIBILITY CATEGORIES Breakpoints are used to define susceptibility and resistance. (In this review, the term susceptibility is preferred over sensitivity ). While these terms should be universally understood, they are frequently used ambiguously because they can refer to the direct interaction between the antibacterial agent and the organism or to the likelihood that the patient will respond to treatment. The first can be measured simply in vitro, while the second involves in vivo complexities such as the dose and dosing schedule, the site of infection, PK of the antibacterial in the individual, and a range of other factors, including the adequacy of host defenses. In some methods, breakpoints are set in such a manner as to create a third category, i.e., intermediate (susceptibility). This category has multiple purposes, including (i) providing a buffer between the resistant and susceptible categories to prevent serious interpretive errors and (ii) implying that the organism is susceptible if the antibacterial is concentrated at the site of infection (e.g., in urine) or suggesting that higher doses of antibacterial should be used where it is safe to do so to achieve efficacy. Two sets of category definitions are given below to accommodate the two types of meanings. In vitro definitions are as follows: susceptible, growth of the bacterial strain is inhibited by an antibacterial agent concentration in the range found for wild-type strains; resistant, growth of the bacterial strain is inhibited by an antibacterial agent concentration higher than the range seen for wild-type strains; and wild type, strains that harbor no acquired resistance mechanism to the antibacterial under question, specifically no resistance attributable to (i) mutation, (ii) acquisition of foreign DNA, (iii) up-regulation of an efflux pump, (iv) up-regulation of target production, or (v) any combination of these. PD and clinical definitions, currently listed in the newly developed international reference method ISO/DIS (78), are as follows: susceptible, the bacterial strain is inhibited by a concentration of an antibacterial agent that is associated with a high likelihood of therapeutic success; intermediate, the bacterial strain is inhibited by a concentration of an antibacterial agent that is associated with an uncertain therapeutic effect; and resistant, the bacterial strain is inhibited by a concentration of an antibacterial agent that is associated with a high likelihood of therapeutic failure. While intuitively appealing, these definitions do not capture all of the concepts embedded in susceptibility categories. A more encompassing set of definitions is provided by the Clinical and Laboratory Standards Institute (CLSI) (28), as follows. The susceptible category implies that isolates are inhibited by the usually achievable concentrations of antimicrobial agent when the recommended dosage (dosage regimen) is used for that site of infection. The intermediate category includes isolates with antimicrobial agent MICs that approach usually attainable blood and tissue levels and for which response rates may be lower than those for susceptible isolates. The intermediate category implies clinical efficacy in body sites where the drugs are physiologically concentrated (e.g., quinolones and -lactams in urine) or when a higherthan-normal dosage of a drug can be used (e.g., -lactams). The category also includes a buffer zone which should prevent small, uncontrolled technical factors from causing major discrepancies in interpretations, especially for drugs with narrow pharmacotoxicity margins. The resistant category implies that isolates are not inhibited by the usually achievable concentrations of the agent with normal dosage schedules and/or demonstrate MICs/zone diameters that fall in the range where specific microbial resistance mechanisms (e.g., -lactamases) are likely and that clinical efficacy against the isolate has not been shown reliably in treatment studies. ORGANIZATIONS THAT SET BREAKPOINTS The processes by which breakpoints are determined can vary widely between susceptibility testing methods. Frequently, these processes are not made explicit in the documentation of the method. For those methods that describe breakpoints without explanation of how the breakpoints are derived, it is assumed that the categories of susceptible, intermediate, and resistant are set using the wild-type cutoff. Methods with recently published breakpoints are outlined in Table 1. Only two international standard-setting groups, the Clinical and Laboratory Standards Institute (CLSI; formerly known as the NCCLS) and the European Union Committee on Antimicrobial Susceptibility Testing (EUCAST), have published guidelines on which data are required for, and how these data are applied to, breakpoint setting (24, 81). The U.S. Food and Drug Administration also sets breakpoints for antibacterials at the time of their approval for use. Unfortunately, breakpoints developed by various organizations may differ, creating confusion for clinical microbiologists, antibacterial susceptibility testing device manufacturers, and clinicians. Harmonization of breakpoints among these organizations should clearly be the aim, taking into account possible differences in doses and dosing schedules used in different parts of the world. THE NATURE OF MICs MICs, as currently measured, are presently the simplest estimates we have of the antibacterial effect in vitro. They are only semiquantitative (see below), yet they have significant utility. There is currently no better measure of antibacterial effect. All breakpoints are either MICs or zone diameter values correlated with MICs. As a consequence, an understanding of the nature of the MIC is fundamental to breakpoint setting. The central concept of an MIC is that it is a measurement of the activity of an antibacterial agent against an individual strain of an organism. It has become the reference measuring tool for susceptibility testing. The value of MIC measurement is frequently criticized because of the unnatural conditions under which it is performed, but that criticism misses the point. It is unnecessary for it to reflect exactly the conditions at the site of infection, and of course in most circumstances it cannot. Hence, the common practice of comparing MICs with levels measured in various body compartments is qualitative at best.

3 VOL. 20, 2007 SETTING ANTIBACTERIAL SUSCEPTIBILITY BREAKPOINTS 393 TABLE 1. Susceptibility testing methods with recently published breakpoints Organization or test [method reference(s)] Method(s) principal media a Breakpoint-setting parameters [reference(s)] Arbeidsgruppen for antibiotikaspørsmål (Norwegian Working Group on Antibiotics [APA]) (18) British Society for Antimicrobial Chemotherapy (BSAC) (1, 11) Calibrated dichotomous sensitivity test (CDS; promulgated by a single laboratory in Sydney, Australia) (16, 17) Clinical and Laboratory Standards Institute (CLSI) (25, 26, 27, 28) and the U.S. Food and Drug Administration Commissie Richtlijnen Gevoeligheidsbepalingen (CRG) (30, 138, 139, 140) Comité de l Antibiogramme de la Société Française de Microbiologie (CA-SFM) (29) Deutches Institut für Normung (DIN) (44, 45, 46, 49, 50, 51, 52, 53) EUCAST (65, 66, 67) Japanese Society for Chemotherapy (JSC) (79, 80) Rosco Diagnostica (a commercial company based in Denmark) (114) Mesa Española de Normalización de la Sensibilidad y Resistencia a los Antimicrobianos (MENSURA [Spain]) (121) Swedish Reference Group for Antibiotics (SRGA) (123) a PDM, paper disk method. The true value of an MIC is as a measuring tool that generates values to which other parameters, such as PD end points and clinical outcomes, can be reliably compared. This requires that MICs have a reasonable level of reproducibility, a subject that has not received a great deal of attention over the years. Indeed, it is frequently quoted that the error associated with measuring an MIC is plus or minus one twofold dilution. While this can work as a rule of thumb, results from so-called tier 2 studies described by the CLSI (24) for establishing quality control ranges show that precision of MIC measurements can be less than or greater than this, depending on the organism-antibacterial combination (131). The origins of the MIC can be traced back to the original Fleming paper on penicillin (strictly, on cultures of a Penicillium strain) (70). Introduced in this paper were the ideas of (i) serial twofold dilution of an antibacterial agent in broth to measure its activity against different species and (ii) reading the end point by noting the opacity of the broth. For decades, the conventional method of determining MICs was in normal test tubes containing 1 to 2 ml of broth, the so-called macro method (27). In the 1960s, the method was adapted to microtiter trays (23), and this has become the preferred method for performing MIC tests in broth. MICs can also be determined by agar dilution, where the antibacterial is incorporated into agar, again in a twofold dilution series, and the inoculum is spotted onto the agar surface prior to incubation (10, 27). The development and adoption of the serial twofold dilution Disk diffusion Mueller-Hinton or Iso-Sensitest Resistance markers, MIC distributions, PK/ PD, clinical and bacteriological outcomes (13, 14) Agar dilution, broth dilution, broth microdilution, disk PK and protein binding (formula), MIC diffusion IsoSensitest agar and broth distributions (95) Disk diffusion Sensitest agar Principally zone diameter distributions (16) For aerobic and facultative bacteria, broth dilution, broth microdilution, disk diffusion Mueller-Hinton agar and broth; for anaerobic bacteria, agar dilution, broth microdilution supplemented Brucella agar and broth Disk diffusion Iso-Sensitest Agar dilution, broth microdilution, disk diffusion Mueller- Hinton Agar dilution, broth microdilution, disk diffusion Mueller- Hinton Agar dilution, broth dilution, broth microdilution Mueller- Hinton MIC distributions, PK/PD, clinical/bacteriological outcome correlations (24) MIC distributions, PK/PD, clinical and bacteriological outcome correlates (31, 104) MIC distributions, PK, correlation with clinical and bacteriological outcome (95) MIC distributions, PK, correlation with clinical and bacteriological outcome (47, 48, 54) In vitro drug characteristics, MIC distributions, PK/PD, clinical outcome correlations (64) Broth microdilution Mueller-Hinton MIC-clinical outcome correlations (12, 115, 116) Disk (pressed tablet) diffusion Mueller-Hinton, Iso-Sensitest, PDM, and Danish blood agar Disk diffusion, broth dilution, agar dilution Mueller-Hinton Agar dilution, disk diffusion, gradient diffusion Iso-Sensitest Zone diameters are calibrated against a range of different national and international MIC breakpoints as well as unique breakpoints for tests performed on Danish blood agar (114) MIC distributions, PK/PD, clinical and bacteriological outcomes (15, 98) Pharmacological breakpoints with speciesrelated adjustments series for MIC measurement, while originally done for convenience in the macro method, have serendipitously turned out to be valuable from at least one point of view. When the MICs of a particular antibacterial for a large number of strains of a single species are plotted on a histogram, it appears that the wild-type population follows a log-normal distribution (132) (Fig. 1). This means that wild-type MICs appear to be normally distributed on a logarithmic scale; the logarithm to base 2 is the simplest of these scales. Furthermore, strains with the same type of acquired resistance also have a log-normal distribution of MICs. For species in which a single resistance mechanism to an antibacterial predominates, it is therefore usual to see a bimodal distribution. Another important feature of the MIC as we currently measure it is that it actually represents a range of MICs. By way of example, Fig. 1 shows that 51,082 of 71,360 strains of Staphylococcus aureus have a vancomycin MIC of 1 mg/liter. In reality, this represents the individual MICs for those strains, each of which is 0.5 mg/liter and 1 mg/liter. In other words, there are 51,082 strains whose MICs lie in the range of 0.5 to 1 mg/liter. Indeed, it is quite possible to determine MICs between the two conventional twofold dilution series values by setting up such concentrations or by using gradient diffusion products (e.g., Etest [AB Biodisk, Solna, Sweden]). From the clinical and PD perspective, such a discriminatory ability may be quite useful. Indeed, in some settings, it would actually be preferable to have a more finely divided range of MICs than conventional twofold dilutions. For example,

4 394 TURNIDGE AND PATERSON CLIN. MICROBIOL. REV. FIG. 1. MIC distributions for four organism-antimicrobial pairs. In each case, the wild type appears as the log-normally distributed population at the lower MICs. CO WT, calculated wild-type cutoff value. (Generated using data from and reference 132.) Downloaded from actual MICs for amikacin for a Pseudomonas aeruginosa strain of 4 to 8 g/ml will be recorded, using serial twofold dilution series, as 8 g/ml. Yet if the PD parameter that best predicts amikacin success for P. aeruginosa is a ratio of peak concentration achieved to MIC (103), a measured amikacin peak concentration of 40 g/ml and a recorded MIC of 8 g/ml will result in a ratio of 5. However, the true ratio may actually be closer to 10 if the actual MIC was just over 4 g/ml. No commercially available antibacterial susceptibility testing products give a more suitable finely divided range of MICs, and space limitations preventing long ranges of dilutions are an important reason for this. However, their development for research purposes is hindered by a misunderstanding of the precision of current MIC tests, often stated to be plus or minus one twofold dilution, as discussed above. The values generated by MIC tests will of necessity be influenced by the method employed (90). The results may differ by choice of technique (broth macrodilution, broth microdilution, agar dilution, or gradient diffusion), medium (Mueller- Hinton, Iso-Sensitest, or Sensitest medium, lot-to-lot variation, divalent cation concentrations, and the effects of additives, such as blood), inoculum size and concentration, incubation conditions (temperature and duration of incubation), and precision in the preparation of different concentrations of the antibacterial being used. Thus, an MIC is only meaningful when the methods and conditions of the test are known. It is for these reasons that the development of an international standard reference method for determining MICs was recently proposed. The recognition of the effectiveness of international standardization in other areas of scientific measurement has stimulated the development of an International Organization for Standards reference method for antibacterial susceptibility testing using broth microdilution and cation-adjusted Mueller-Hinton medium (78). It was published as an approved standard in late This is just the beginning, as the reference method will not work for all bacteria for which we might want to have MICs. Nevertheless, it is MICs generated using this reference methodology and its future enhancements that will become the standards for MICs in future. DATA NEEDS FOR SETTING BREAKPOINTS A range of data are used to assist breakpoint-setting organizations in selecting breakpoints, including in vitro microbiological data, animal and human PK/PD data, and clinical/bacteriological outcome data from prospective clinical studies. No single set of data provides all the information necessary to make decisions. As pointed out above, some methods concentrate mainly on in vitro data to establish breakpoints. Undoubtedly, human PK is factored into the decision, but how this occurs is not made explicit. We believe that the following four main types of data are necessary for the establishment of appropriate breakpoints: (i) MIC distributions and wild-type cutoffs; (ii) in vitro resistance markers, both phenotypic and ge- on October 5, 2018 by guest

5 VOL. 20, 2007 SETTING ANTIBACTERIAL SUSCEPTIBILITY BREAKPOINTS 395 notypic; (iii) PK/PD data from animal models and human studies; and (iv) outcome data, both clinical and bacteriological, from appropriate clinical studies and the MICs of the causative pathogens in those studies. For establishing zone diameter breakpoints, additional data establishing the relationship between zone diameters and MICs are required. The most important and difficult challenge in using these data is ensuring that there is an appropriate balance between the various forms of data, with consideration being given to the different pathogens and the types and severity of the infections associated with those pathogens. At present, there is no formula that can assist in deciding which data are more important in a particular circumstance. Instead, the final choice of emphasis on the different types of data is made by consensus among standard-setting committees and groups. Such an approach can be divisive, with individuals weighting different data types differently according to their training and skill base. It is essential that the membership of any breakpoint-setting committee include a mix of skills in all four main data areas. Since infections occur in different body compartments, e.g., blood, soft tissues, subarachnoid space, bladder, bone, lungs, or mucosal surfaces, breakpoints, at least in theory, should be developed for each infection syndrome, e.g., bloodstream infection, cellulitis, meningitis, lower urinary tract infection, osteomyelitis, pneumonia, or pharyngitis. This would vastly increase the complexity of breakpoint setting, and therefore almost all methods choose only one set of breakpoints or, sometimes, other sets of breakpoints adjusted to the type of infection in cases where drug concentrations are substantially different, such as urinary tract infection or meningitis. In general, bloodstream infections are considered most generally representative of serious infections, and therefore the PK of the drug in blood is used to set breakpoints. MIC Distributions and Wild-Type Cutoff Values Constructing MIC distributions for organism-antibacterial combinations is the first step in the development of breakpoints for that antibacterial. Broth and/or agar dilution MICs are determined for all organisms of interest, and histograms such as those in Fig. 1 are constructed. Inspection of a histogram gives an immediate picture of whether only wild-type strains are present or whether strains with abnormally elevated MICs are also included. Ideally, these histograms should be constructed on a species-by-species basis because it is unlikely that even related species will have exactly the same modal MIC or wild-type range of MICs (or same mean and standard deviation on the log-normal scale). Often, species are combined for convenience, such as coagulase-negative staphylococci or Enterobacter species, but in general it is better to avoid this where possible. It is also desirable that the full range of the wild-type MIC distribution is included in the antibacterial dilution series and that the MICs are not truncated at one or the other end of the distribution. From the full range of the wild-type MIC distribution, simple inspection will often allow one to estimate where the upper end of the wild-type distribution ends and thus to define wild-type cutoff values, with the wild type being defined as above. EUCAST has released tables and histograms showing a range of wild-type MIC distributions for many organisms and antibacterials. They are freely available on the Internet at The data are a collation of MICs collected from a wide array of national and international studies using defined methods. If the full span of wild-type MICs is available, it is also amenable to statistical analysis. Using this type of data, statistical techniques that can reliably determine wild-type cutoff values have recently been developed, thus eliminating the need to estimate cutoff values by inspection (92, 132). This is particularly useful for distributions where there is apparent overlap between the wild-type distribution and the distribution of abnormal strains, such as the example of Pseudomonas aeruginosa and gentamicin in Fig. 1. The first of these methods uses an iterative process to find the optimum fit of the cumulative distribution MICs and works readily on standard twofold MICs. The second method uses a reflection of the lower half of the wild-type distribution about the estimated mean and requires intermediate values between the standard twofold dilution series to be effective. It was adapted from an original method used to determine zone diameter ranges and interpretive criteria of susceptible strains (92, 91). In setting breakpoints, it is generally considered inappropriate to select values that fall inside the wild-type range. Placing breakpoints above or below wild-type distributions creates no problems with interpretation. However, when breakpoints fall inside wild-type distributions, it creates the somewhat anomalous splitting of strains into susceptible and resistant strains even though the latter do not have an acquired resistance mechanism. In some cases, such a split cannot be avoided, because the desire is to have as few breakpoints as possible for a given antibacterial agent and for some species the wild-type MIC distribution is elevated compared to that for most other susceptible species. A typical example of this type of splitting is seen with Stenotrophomonas maltophilia and ceftazidime, where the wild-type distribution ranges from values within reach of those achievable clinically to values well above those that can be achieved with maximum doses. Phenotypic and Genotypic Resistance Markers It is frequently possible to identify the existence of a resistance mechanism in a bacterial isolate by methods other than measuring the MIC (or zone diameter). These methods can be phenotypic or genotypic. Phenotypic methods include (i) direct detection of degrading enzymes (e.g., -lactamase testing); (ii) screening plates by using a concentration lower than the breakpoint (e.g., extended-spectrum -lactamase screening); (iii) medium modification to enhance resistance expression (e.g., use of brain heart infusion agar to detect vancomycin-resistant enterococci); (iv) modification of incubation conditions to enhance resistance expression (e.g., incubation at 30 to 35 C to detect methicillin resistance in staphylococci); (v) -lactamase confirmation by the use of specific inhibitors, such as clavulanate or EDTA; (vi) induction tests (e.g., macrolide induction of clindamycin resistance); (vii) direct detection of the protein conferring resistance (e.g., agglutination detection of penicillin-binding protein 2a in Staphylococcus aureus); and (vii) detection of resistance to high levels of an antibacterial (e.g., high-level aminoglycoside resistance in enterococci) (124).

6 396 TURNIDGE AND PATERSON CLIN. MICROBIOL. REV. Genotypic tests are usually reserved for confirmation of phenotypic resistance (112). The one common exception is the detection of meca in staphylococci by PCR methods. This gene encodes the altered penicillin-binding protein 2a and is associated with methicillin resistance. This association holds strongly for S. aureus, where the detection of meca correlates strongly with MICs that are greater than the wild-type cutoff value (oxacillin MIC, 2 mg/liter [CLSI]). Problems with the correlation in coagulase-negative staphylococci led to the significant lowering of the oxacillin breakpoint to 0.5 mg/liter by the CLSI to achieve greater correlation with the presence of meca (28). This was precautionary, as there are no clinical data available to confirm or refute whether strains of coagulasenegative staphylococci with a MIC of 1 or 2 mg/liter will respond to treatment. If simple special phenotypic (i.e., other than the MIC itself) or genotypic methods can be developed to detect acquired resistance, then ideally these should be used to validate the choice of wild-type cutoff values. Strains with MICs at and to either side of the wild-type cutoff should be subjected to those special phenotypic and/or genotypic tests to ensure correlation. If necessary, the wild-type cutoff should be adjusted to that defined by the presence of the resistance mechanism. Most methods that recommend the use of special phenotypic and/or genotypic tests also recommend that the isolate be reported as resistant regardless of the conventional MIC-based susceptibility result. Put another way, the recommendation is that the isolate should be interpreted as resistant, the usual breakpoint does not apply, and the MIC result should be ignored. The validity of this approach has rarely been tested and would be difficult to test except in animal models. Instead, the conservative view is taken that in vitro detection of resistance to an antibacterial by additional special phenotypic and/or genotypic tests should preclude the use of that antibacterial (see the example above with meca and coagulase-negative staphylococci). Fortunately, for many organism-antibacterial combinations, the relationship between special phenotypic and/or genotypic tests, elevated MICs, and poor treatment outcomes appears convincing. There are some notable exceptions, such as Streptococcus pneumoniae and penicillin, where the resistance phenotype and genotype, that is, the presence of altered penicillin-binding proteins and/or the genes encoding them, do not appear to correlate well with outcomes (68, 108, 142), except in cases of meningitis. This situation provides a conundrum when breakpoints have already been set for a particular organism-antibacterial combination. New resistance mechanisms are frequently detected only after an antibacterial has been in clinical use for some time. These may represent examples of hidden resistance whereby resistance mechanisms are detected genotypically in organisms that have MICs within the susceptible range. Examples of hidden resistance which have not been addressed completely by organizations which set breakpoints include quinolone target mutations in gram-negative bacilli (42) and Streptococcus pneumoniae (43), extended-spectrum beta-lactamases in organisms that also constitutively produce AmpC (125), and metallo-beta-lactamase production by gram-negative bacilli (73). At the local level, clinical laboratories may be faced with a dilemma whereby resistance mechanisms are confirmed to be occurring by genotypic methods yet no review of breakpoints has been made by CLSI or EUCAST. In some circumstances, phenotypic methods may have been described to assist in detection of these resistance mechanisms. However, we caution that such methods are often based on organisms from a limited geographic area and may not be universally applicable. Their greatest use may be for epidemiologic purposes rather than for communication to prescribers. PK/PD Considerations With the development of our understanding in the PD of antibacterials, one of the major benefits has been its application to the setting of breakpoints (4, 60, 105). PD is the study of drug effects over time and is therefore intimately linked with the changes in drug concentrations over time, namely, PK. The terms are usually linked to create the term PK/PD. For antibacterials, PK/PD is the study of the relationship between PK variables and microbial inhibition or killing in vivo and, by extension, clinical outcome (41, 58). Extensive studies in animal and in vitro PK models over the last 20 years have led to a clear understanding of PK/PD relationships of many classes of antibacterials, including the -lactams, aminoglycosides, quinolones, macrolides, lincosamides, tetracyclines, and glycopeptides. Clinical studies supporting the evidence generated in animal models have been conducted for -lactams, aminoglycosides, and fluoroquinolones (2). In vitro studies. The PD properties of an antibacterial agent are initially explored in vitro. Bacterial inhibition and killing are initially examined by exposing species of interest to a range of fixed drug concentrations, e.g., different multiples of the MIC, and measuring viable counts over a number of hours (33). The following two major patterns are found: concentration-dependent killing, where killing becomes more rapid and profound with increasing drug concentrations; and concentration-independent or time-dependent killing, where no further increase in the rate of killing is seen for concentrations much above the MIC (33). The second in vitro phenomenon of importance is the so-called postantibiotic effect (PAE). This is a period of delayed regrowth, following drug removal, after brief periods of exposure to an antibacterial in vitro. Again, two major patterns emerge, as follows: a moderate to long delay in regrowth (prolonged PAE) and immediate regrowth or only a short delay (minimal PAE) (40). Other persistent antibacterial effects have been described, such as postantibiotic leukocyte enhancement (97) and postantibiotic sub-mic effects (106). None of these other effects have added greatly to our understanding of the action of antibacterials in vivo or provided information that would be predictive of clinical efficacy. Demonstration of in vitro PAE requires validation in an animal model, because the persistent effect observed in vitro may occasionally be absent in vivo (1, 134). However, in most circumstances, a combination of the bacterial killing and PAE patterns is sufficient to describe in vitro PD properties and to make predictions about in vivo PK/PD properties, as summarized in Table 2. In vitro studies can be extended to sophisticated PD models, where the PK of drugs in humans are simulated and the effects on bacterial killing measured (96). Most studies using this technique have focused on addressing specific questions rather than defining the fundamental PD properties of drugs. In other

7 VOL. 20, 2007 SETTING ANTIBACTERIAL SUSCEPTIBILITY BREAKPOINTS 397 TABLE 2. PD properties of various antibacterial classes Killing/inhibition pattern PAE In vivo PK/PD parameter predicting efficacy Antibiotic class(es) a Time dependent Minimal %T MIC -Lactams Minimal AUC/MIC ratio Linezolid* Prolonged AUC/MIC ratio Macrolides, lincosamides, tetracyclines Prolonged C max /MIC ratio Glycopeptides* Concentration dependent Minimal AUC/MIC ratio Polymyxins Prolonged AUC/MIC ratio and/or C max /MIC ratio Aminoglycosides, quinolones, streptogramins, ketolides, daptomycin a *, the PK/PD parameter predicting efficacy varies with the organism under study and the type of animal model (1, 88, 89). cases, they have confirmed earlier findings from animal models. In vitro PD model studies have been helpful in one area, namely, defining the importance of a regimen in preventing the emergence of resistant subpopulations (19, 69). For a small number of bacterium-drug interactions, selection of resistant subpopulations present in the original bacterial population is a problem. The most notable of these is aminoglycosides and Pseudomonas aeruginosa (76). Animal model studies. Studies with animal models have been critical to our understanding of PK/PD relationships. The earliest work, by Eagle and colleagues, showed the importance of the dosing interval in determining the efficacy of penicillin (61). It took another 35 years before progress was made in this area, when the neutropenic mouse thigh and pneumonia models were employed for the first time to define possible PD parameters predicting efficacy (94, 135). These studies were the first to clearly define the relationship between PK parameters and bacterial killing in tissues by using PD principles. They successfully showed that different classes of antibacterials could have different predictors of efficacy. The principal value of these two models has been to assist in defining PK/PD parameters for the different drug classes, as they were not designed to directly reflect the clinical picture in humans. The three PK/PD predictors of efficacy for which relationships have been shown are (i) time above the MIC (T MIC), usually expressed as a percentage of the dosing interval; (ii) the ratio of the area under the curve over 24 h to the MIC (AUC 24 / MIC); and (iii) the peak level-to-mic ratio (C max /MIC). In this context, the AUC is the area found under the plasma concentration-time profile, and the C max is the maximum plasma concentration after each dose. The choice of the three PK components T MIC, AUC, and C max rather than other PK components relates to the fact that these components have relatively low interdependence, i.e., they can vary significantly without greatly affecting each other. Thus, dosing regimens can be varied in animal model studies so widely that it is possible to minimize the effect of interdependence and thereby maximize the chance that only one of the components will be highly statistically correlated. An example of this is shown in Fig. 2, showing significant scatter and poor correlation between AUC 24 /MIC or C max /MIC and killing but excellent correlation to T MIC for one organism-antibacterial pair in the mouse pneumonia model. Note that MIC appears in each of the PK/PD parameters. For the utility of PK/PD data in setting breakpoints, this was a pivotal finding. When the PK/PD parameters are controlled for by using MIC in the denominator, it is possible to show that similar agents have similar parameter magnitudes for the same degree of killing when MIC differences between these agents are factored into the analysis (36, 37, 137). Another important feature of the PK/PD parameters is that correspondences between drugs are best when protein binding is taken into account (1, 37). For instance, the T MIC percentages for maximum killing of Staphylococcus aureus in the mouse thigh model are identical for cefotaxime and ceftriaxone, but only when free drug concentrations are used in the analysis (37). This is strong evidence that protein binding must be taken into account when applying PK/PD findings to the setting of breakpoints. Questions arise regarding the applicability of PK/PD parameter data generated in animal models such as the mouse thigh model, where the end point is bacteriostasis or bacterial killing after 24 h. Some confidence about the parameters and their FIG. 2. Relationship between PK/PD parameters and killing of Klebsiella pneumoniae by cefotaxime in the mouse pneumonia model. (Reprinted from reference 37 with permission from Elsevier.)

8 398 TURNIDGE AND PATERSON CLIN. MICROBIOL. REV. TABLE 3. Prospective clinical studies of PK/PD parameters Agent Infection(s) c PK/PD parameter selected Magnitude of parameter for maximum efficacy (clinical cure rate) f -Lactams Cefmenoxime Nosocomial pneumonia Time above DRC a % 118 Cefepime Hospitalized, various Time above 4.3 MIC 100% 126 Penicillins and cephalosporins b Otitis media caused by Streptococcus pneumoniae or Haemophilus influenzae Time above MIC 60% 38 Reference(s) Fluoroquinolones Ciprofloxacin Mainly nosocomial pneumonia AUC 24 /MIC ratio Levofloxacin Serious community-acquired infection C max /MIC ratio Gatifloxacin and Streptococcus pneumoniae communityacquired fuauc 24 /MIC ratio d levofloxacin pneumonia and AECB Grepafloxacin AECB AUC 24 /MIC ratio Garenoxacin Community-acquired pneumonia, AECB, and None e 133 sinusitis Levofloxacin Nosocomial pneumonia AUC 24 /MIC ratio Ciprofloxacin Pseudomonas aeruginosa bacteremia C max /MIC ratio Other agents Gentamicin Nosocomial pneumonia C max /MIC ratio Gentamicin and Pseudomonas aeruginosa bacteremia C max /MIC ratio tobramycin Vancomycin Staphylococcus aureus lower respiratory tract infection AUC 24 /MIC ratio , 102 a DRC was used as a surrogate of the MIC per reference 128. b This was an analysis of data from multiple clinical studies. c AECB, acute exacerbations of chronic bronchitis. d fu, fraction unbound. e Due to the very high activity of the agent, fuauc 24 /MIC ratios were 200 in more than 90% of patients. f Magnitudes are expressed in terms of total drug, rather than unbound drug, unless stated otherwise. magnitudes can be taken from analyses that have compared them with mortality in a range of animal models (39) and across different organisms (41). The magnitudes of the relevant parameters predicting efficacy are thought to vary depending on a range of factors, with the most important being the class of drug and the infection compartment. T MIC percentages to achieve bacteriostasis (no net growth or killing at the site of infection in the animal model) vary between -lactam classes, with cephalosporins requiring the highest percentage and carbapenems the lowest. Variation between bacterial species is sometimes seen. Clinical studies of PD. There are few clinical studies that have been conducted deliberately to confirm the findings of in vitro and animal model PD. This is understandable because in order to confirm the choice of PK/PD parameters, it is necessary to have both clinical and microbiological failures, as well as sufficient variation in dosing regimens, to separate out the correct PK/PD parameter. Planning for failures in studies is generally considered unethical, and wide variation in dosing regimens is impractical. Hence, most studies in this area have been retrospective, although a few key ones have analyzed outcomes prospectively based on PK/PD parameters (2). Those that have been conducted prospectively have confirmed the analyses made in vitro and with animal PK/PD models. A number of clinical studies have shown a relationship between MIC and treatment outcome. Conditions and agents studied include suspected gram-negative bacteremia and cefoperazone (56), multiple different infections and cefoperazone (36) or cefotaxime (55), acute otitis media and cefuroxime axetil (75), Bacteroides fragilis and cefoxitin (120), methicillin-resistant Staphylococcus aureus bacteremia and vancomycin (117), and extended-spectrum -lactamase-producing gram-negative bacteremia and third-generation cephalosporins (83). By itself, this relationship is informative about the importance of the MIC but otherwise unhelpful because it is not possible to know which of the three PK/PD parameters is relevant. This comes about because dosage regimens in these studies are usually fixed or vary little, making it impossible to control for the level of interdependence of the PK/PD variables. Furthermore, the MIC of the infecting pathogen and the dosing schedule of the antibacterial agent are not the only determinants of efficacy, and the host response can be important or even dominant in the resolution of infection. Most importantly, we cannot determine from such studies whether the dosing schedules used were optimal. If the PK/PD parameters are known, the effects of changes in the dosing schedule can be predicted and dosing schedules optimized. Fortunately, there are some studies that have been able to examine which of the PK/PD parameters are important. These are listed in Table 3. In each case, the selected parameter and its magnitude correlate reasonably well with those found in animal models. These data strengthen the case for using PK/PD parameters and their magnitudes for estimation of breakpoints that have clinical meaning. Estimation of target attainment. Initial attempts at using PK/PD data to estimate breakpoints used average PK values (129). This is unsatisfactory because of significant intersubject

9 VOL. 20, 2007 SETTING ANTIBACTERIAL SUSCEPTIBILITY BREAKPOINTS 399 Downloaded from FIG. 3. (A) Fractional probability of target attainment (expressed as a percentage) of intravenous levofloxacin at 500 mg daily, based on PK in healthy volunteers. (Generated using data from reference 22.) (B) Fractional probability of target attainment of intravenous levofloxacin at 750 mg daily, based on PK in a clinical efficacy study, compared to MIC distributions of two major nosocomial pneumonia pathogens. (Reprinted from reference 59 with permission by the Infectious Diseases Society of America. All rights reserved.) variability in PK, even in young healthy subjects (58). PK values can vary even more during illness and as a consequence of underlying diseases. Both kinds of variation need to be included in any estimation of breakpoints based on PK/PD data. The method for factoring in variation is known as Monte Carlo simulation and was first successfully employed in antibacterial PD by Drusano et al. (57, 58). Monte Carlo simulation is a statistical technique whereby a population of values of interest is simulated using existing data, such as the mean and standard deviation from a standard (small) PK/PD study, and then randomly generating a large number of patient values according to an underlying statistical distribution, such as the normal (Gaussian) or, sometimes, log-normal distribution. In this way, the variation in time above a certain value, the AUC 24, and C max,as found for patients given a defined dosage regimen, can be created and used to estimate the probabilities of reaching certain values when large numbers of patients are treated with that regimen. In the context of antibacterials, these certain values will be determined by the magnitude of the PK/PD parameter predicting maximum efficacy, for example, 50% for %T MIC of a -lactam, 100 for an AUC 24 / MIC ratio of a fluoroquinolone, or 10 for a C max /MIC ratio of an aminoglycoside. With these values, it is possible to find the probability that these magnitudes will be reached at different MICs. This whole process is called target attainment analysis. When target attainment rates fall below 90%, the probability of that dosing regimen being effective is significantly diminished, and the PK/PD cutoff becomes the highest MIC for which the target attainment exceeds 90%. The figure of 90% has been chosen arbitrarily and has not been validated clinically but is widely used in target attainment analysis. By way of example, using healthy volunteer PK/PD data for intravenous levofloxacin at a dose of 500 mg daily (22), the on October 5, 2018 by guest

10 400 TURNIDGE AND PATERSON CLIN. MICROBIOL. REV. fractional probabilities of achieving an AUC 24 /MIC ratio of at least 100 for different MICs are shown in Fig. 3A. Improvements on this basic process can be made when (i) PK data from prospective clinical studies are used, rather than conventional PK data from volunteer studies (58); and (ii) MIC distributions, as described above, are factored into the analysis (59). These improvements are shown in Fig. 3B, from a study of intravenous levofloxacin (750 mg once daily) for treatment of nosocomial pneumonia (59). Monte Carlo simulation is being used increasingly to assist in developing clinical breakpoints (5, 105). It is now recognized that including between-patient variation in PK and betweenorganism variation in MICs is an essential component that must be taken into account when setting breakpoints. It is no longer appropriate to use average PK/PD values or susceptibility measures such as the MIC 90 (1). Results of Monte Carlo simulation may vary according to the input variables and underlying assumptions used. The input variables, as mentioned above, include a variety of PK parameters and the extent of protein binding of the antibacterial. Clearly, such variables may differ from source study to study. The adequacy of these studies may influence the outcome of the Monte Carlo simulation. Even the use of apparently equivalent parameters, such as half-life versus clearance, in different models may greatly alter the results. The number of patient simulations is also of relevance; typically, 5,000 to 10,000 such simulations are used. A number of different programs are used to conduct Monte Carlo simulation (such as Adapt, Crystal Ball, etc.), but it is unlikely that the use of different programs will substantially alter the results of target attainment analysis. We believe that standard, predefined methodologies for Monte Carlo simulation should be used by organizations setting breakpoints and that these methods should be internationally harmonized. Outcome Data from Clinical Studies Some organizations give the greatest weight to prospective clinical studies to define breakpoints, especially those trials done for regulatory purposes. Such trials are typically randomized controlled trials with predefined dosage regimens, clinical end points, and bacteriologic end points. An advantage of such studies is that concomitant antibacterial use is typically minimal so that the effects of the antibacterial under study can be studied independently. Most often, the appropriate correlation in such studies is clinical and/or bacteriological outcome versus the MIC for the infecting pathogens. Clinical studies may have their greatest value in providing a reality check for cutoffs derived from microbiologic and PK/PD studies. Clinical failure rates in excess of those predicted by microbiological and PK/PD data should trigger a reassessment of breakpoints prior to an antibacterial s commercial introduction. It is important to note that there is a range of problems with regulatory clinical studies that limit our ability to always correctly interpret these data for breakpoint setting. These limitations are as follows. (i) The great majority of studies predefine resistance and exclude/withdraw patients with resistant strains. The way resistance is predefined is often not clear to the investigators but often appears to be based, at least in part, on MIC distributions, i.e., the wild-type cutoff for pathogens likely to be involved in the infection under study. (ii) As a consequence of point i and the nature of MIC distributions, it is likely that for some species, there will be few infections included in the study caused by strains at the top end of the distribution, especially because sample sizes for an individual species are likely to be small. This greatly enhances the chance of error in estimating a clinical breakpoint. (iii) There is little consensus on which rates of cure and/or eradication are acceptable. From a regulatory standpoint, such studies typically test noninferiority compared to a drug that has previously received regulatory approval. It is not clear whether this is always the appropriate end point for assessment of breakpoints. Indeed, regulators have recently recognized the limitations of comparative noninferiority studies and in some cases are now requesting studies designed to show superiority or to be placebo controlled. (iv) No numerical account is taken of natural response rates, which can be quite high for some common bacterial infections. (v) Not all species of interest in a particular infection necessarily get included in prospective studies. Thus, breakpoints may be extrapolated inappropriately from commoner species. Ideally, clinical studies would recruit significant numbers of cases where the infecting pathogen has an MIC on either side of the wild-type and PK/PD cutoff values to assist in the clinical validation of final breakpoints. However, this is unlikely to happen for a range of reasons, including point ii above and the fact that the only studies with sufficient recruitment are those conducted on new, potent antibacterial agents for regulatory registration purposes. Of interest, the use of clinical outcome data to define breakpoints has been popular in the past in Japan. Examination of outcomes by MIC has led to the selection of a breakpoint estimated to be the lowest MIC where maximum or near-maximum efficacy has been achieved (115,116). COMBINING CUTOFFS TO SET BREAKPOINTS General Principles The most comprehensive process for setting MIC breakpoints involves a comparison between PK/PD cutoffs, clinical cutoffs, and wild-type cutoffs. Ideally, it is based on a full set of data, as outlined in Table 4. Combining cutoffs into a single breakpoint is still largely a matter of judgment. The process, unfortunately, can be swayed by the composition of skills and biases of the group making the breakpoint decisions. It is largely a question of how to merge the three cutoffs in such a way as to ensure that (i) strains that are likely to respond to treatment with the chosen dosage schedule at the likely site of infection are classified as susceptible and (ii) strains that are unlikely to respond to treatment with the chosen dosage schedule at the likely site of infection are classified as resistant. It is our opinion that the initial step in breakpoint determination should be an assessment of the MIC distribution for a contemporary collection of isolates obtained from global sources. The PK/PD cutoff should be applied to this collection. We believe that the PK/PD cutoff provides the greatest amount of value in this situation because it includes much of the relevant data in its construction, including (i) the MIC as it is measured in vitro; (ii) the relevant PD parameter and its mag-